Seagrass_biomass_regression_2017
Lia Domke
10/21/2019
We’re curious to see if there is a relationship between longest leaf length to total shoot biomass. Data available: In 2017 21 sites were surveyed for seagrass. At each site, 8 0.25m^2 quadrats were surveyed and 5 entire shoots (i.e. multiple leaves) were removed and brought back to the lab to be measured (in cm). Each shoot (n = 840) was weighed to get total fresh and dry biomass (in grams) and each leaf on the shoot had its length (cm) and width (cm) measured.
We are interested in the length of the longest leaf and the dry biomass. # Data import
eelgrass <- read.csv("..//ALL_DATA/seagrass_biometrics_CLEAN.csv")Libraries
## ── Attaching packages ────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──## ✔ ggplot2 3.2.1 ✔ purrr 0.3.2
## ✔ tibble 2.1.3 ✔ dplyr 0.8.3
## ✔ tidyr 1.0.0 ✔ stringr 1.4.0
## ✔ readr 1.3.1 ✔ forcats 0.4.0## ── Conflicts ───────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## selectData Cleaning
# extract what we're interested in
str(eelgrass)## 'data.frame': 840 obs. of 71 variables:
## $ site : Factor w/ 21 levels "2017_H_01","2017_H_02",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ collection_date : Factor w/ 18 levels "20/8/17","21/8/17",..: 18 18 18 18 18 18 18 18 18 18 ...
## $ YYYYMMDD : int 20170429 20170429 20170429 20170429 20170429 20170429 20170429 20170429 20170429 20170429 ...
## $ quadrat : int 1 1 1 1 1 2 2 2 2 2 ...
## $ plant : int 1 2 3 4 5 1 2 3 4 5 ...
## $ rhi_length : num 5 5 5 4.6 5 4.6 4 5 5 3.6 ...
## $ node1 : num 0.5 0.7 0.3 0.4 0.2 1.2 0.3 0.3 0.5 0.4 ...
## $ node2 : num 0.4 0.4 0.3 0.4 0.2 0.6 0.3 0.2 0.6 0.6 ...
## $ node3 : num 0.3 0.3 0.4 0.3 0.3 0.4 0.2 0.2 0.4 0.4 ...
## $ node4 : num 0.2 0.3 0.3 0.4 0.2 0.3 0.2 0.2 0.3 0.2 ...
## $ node5 : num 0.2 0.3 0.2 0.2 0.3 0.3 0.2 0.3 0.2 0.2 ...
## $ leaf_length1 : num 22.1 25.2 16.4 21 13 32.4 23 16.5 26 26.5 ...
## $ leaf_length2 : num 18.9 17.6 9.8 18 13.6 22.1 12.3 21.1 21.1 18.1 ...
## $ leaf_length3 : num 14.5 22 14.1 8.5 9.6 25 16.1 22.7 20.2 25.7 ...
## $ leaf_length4 : num 6.4 14.9 9.4 17 NA 27.3 9.3 8.7 10.2 11.3 ...
## $ leaf_length5 : num 13.3 8 NA 16.5 NA 12.4 15.5 15.5 17.5 22.7 ...
## $ leaf_length6 : num NA 3.6 NA NA NA 9.2 NA NA NA NA ...
## $ leaf_length7 : num NA NA NA NA NA 6 NA NA NA NA ...
## $ leaf_length8 : num NA NA NA NA NA NA NA NA NA NA ...
## $ leaf_length9 : num NA NA NA NA NA NA NA NA NA NA ...
## $ leaf_length10 : num NA NA NA NA NA NA NA NA NA NA ...
## $ leaf_width1 : num 0.3 0.3 0.2 0.3 0.2 0.4 0.2 0.2 0.3 0.4 ...
## $ leaf_width2 : num 0.2 0.3 0.2 0.2 0.2 0.4 0.2 0.2 0.2 0.3 ...
## $ leaf_width3 : num 0.2 0.3 0.2 0.2 0.2 0.4 0.2 0.2 0.3 0.3 ...
## $ leaf_width4 : num 0.2 0.3 0.2 0.2 NA 0.4 0.2 0.2 0.3 0.3 ...
## $ leaf_width5 : num 0.2 0.3 NA 0.2 NA 0.2 0.3 0.3 0.2 0.4 ...
## $ leaf_width6 : num NA 0.2 NA NA NA 0.2 NA NA NA NA ...
## $ leaf_width7 : num NA NA NA NA NA 0.3 NA NA NA NA ...
## $ leaf_width8 : num NA NA NA NA NA NA NA NA NA NA ...
## $ leaf_width9 : num NA NA NA NA NA NA NA NA NA NA ...
## $ leaf_width10 : num NA NA NA NA NA NA NA NA NA NA ...
## $ pad_mass_g : num 0.543 0.529 0.536 0.491 0.592 ...
## $ pad_epiphyte_mass_g : num 0.549 0.53 0.543 0.494 0.596 ...
## $ rhi_mass_fw : num 0.227 0.488 0.346 0.397 0.455 ...
## $ rhi_foil : num 0.658 0.427 0.45 0.376 0.55 ...
## $ rhi_foil_dw_g : num 0.684 0.478 0.49 0.411 0.586 ...
## $ shoot_mass_fw : num 0.269 0.412 0.165 0.321 0.116 ...
## $ shoot_foil : num 0.905 0.728 0.758 0.611 0.708 ...
## $ shoot_foil_dw : num 0.944 0.791 0.783 0.66 0.724 ...
## $ xs_shoot_mass_fw : num 4.77 NA NA NA NA ...
## $ xs_shoot_foil : num 0.789 NA NA NA NA ...
## $ xs_shoot_foil_dw : num 1.38 NA NA NA NA ...
## $ xs_pad_mass_g : num NA NA NA NA NA NA NA NA NA NA ...
## $ xs_epiphyte_pad_mass_g: num NA NA NA NA NA NA NA NA NA NA ...
## $ iso_count : int NA NA NA NA NA NA NA NA NA NA ...
## $ iso_fw : num NA NA NA NA NA NA NA NA NA NA ...
## $ iso_foil : num NA NA NA NA NA NA NA NA NA NA ...
## $ iso_foil_dw : num NA NA NA NA NA NA NA NA NA NA ...
## $ gamm_amph_count : int 3 NA NA NA NA 21 NA NA NA NA ...
## $ gamm_amph_fw : num 0.0059 NA NA NA NA 0.036 NA NA NA NA ...
## $ gamm_amph_foil : num 0.495 NA NA NA NA ...
## $ gamm_amph_foil_dw : num 0.497 NA NA NA NA ...
## $ caprel_count : int NA NA NA NA NA NA NA NA NA NA ...
## $ caprel_fw : num NA NA NA NA NA NA NA NA NA NA ...
## $ caprel_foil : num NA NA NA NA NA NA NA NA NA NA ...
## $ caprel_foil_dw : num NA NA NA NA NA NA NA NA NA NA ...
## $ limpet_count : int NA NA NA NA NA NA NA NA NA NA ...
## $ limpet_fw : num NA NA NA NA NA NA NA NA NA NA ...
## $ limpet_foil : num NA NA NA NA NA NA NA NA NA NA ...
## $ limpet_foil_dw : num NA NA NA NA NA NA NA NA NA NA ...
## $ gastropod_count : int NA NA NA NA NA NA NA NA NA NA ...
## $ gastropod_fw : Factor w/ 80 levels "","0.0011","0.0012",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ gastropod_foil : num NA NA NA NA NA NA NA NA NA NA ...
## $ gastropod_foil_dw : num NA NA NA NA NA NA NA NA NA NA ...
## $ crab_count : int NA NA NA NA NA NA NA NA NA NA ...
## $ crab_fw : num NA NA NA NA NA NA NA NA NA NA ...
## $ crab_foil : num NA NA NA NA NA NA NA NA NA NA ...
## $ crab_foil_dw : num NA NA NA NA NA NA NA NA NA NA ...
## $ polychaete_count : int 6 NA NA NA NA 7 NA NA NA NA ...
## $ other_animal_notes : Factor w/ 33 levels "","\"crab\" is tellmesus",..: 1 1 1 1 1 12 1 1 1 1 ...
## $ notes : Factor w/ 5 levels "","caprel insignificant weight",..: 1 1 1 1 1 1 1 1 1 1 ...eel <- eelgrass %>%
mutate(shoot_dw = shoot_foil_dw-shoot_foil) # calculate dry weight
# determin max leaf length
eel2 <- eel %>%
rowwise() %>%
mutate(max_length = max(leaf_length1, leaf_length2, leaf_length3, leaf_length4, leaf_length5, leaf_length6, leaf_length7, leaf_length8, leaf_length9, leaf_length10, na.rm=TRUE)) %>%
mutate(mean_length = mean(leaf_length1, leaf_length2, leaf_length3, leaf_length4, leaf_length5, leaf_length6, leaf_length7, leaf_length8, leaf_length9, leaf_length10, na.rm=TRUE))## Warning in max(leaf_length1, leaf_length2, leaf_length3, leaf_length4,
## leaf_length5, : no non-missing arguments to max; returning -Inf
## Warning in max(leaf_length1, leaf_length2, leaf_length3, leaf_length4,
## leaf_length5, : no non-missing arguments to max; returning -Inf
## Warning in max(leaf_length1, leaf_length2, leaf_length3, leaf_length4,
## leaf_length5, : no non-missing arguments to max; returning -Inf
## Warning in max(leaf_length1, leaf_length2, leaf_length3, leaf_length4,
## leaf_length5, : no non-missing arguments to max; returning -Inf
## Warning in max(leaf_length1, leaf_length2, leaf_length3, leaf_length4,
## leaf_length5, : no non-missing arguments to max; returning -Inf
## Warning in max(leaf_length1, leaf_length2, leaf_length3, leaf_length4,
## leaf_length5, : no non-missing arguments to max; returning -Inf
## Warning in max(leaf_length1, leaf_length2, leaf_length3, leaf_length4,
## leaf_length5, : no non-missing arguments to max; returning -Infstr(eel2)## Classes 'rowwise_df', 'tbl_df', 'tbl' and 'data.frame': 840 obs. of 74 variables:
## $ site : Factor w/ 21 levels "2017_H_01","2017_H_02",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ collection_date : Factor w/ 18 levels "20/8/17","21/8/17",..: 18 18 18 18 18 18 18 18 18 18 ...
## $ YYYYMMDD : int 20170429 20170429 20170429 20170429 20170429 20170429 20170429 20170429 20170429 20170429 ...
## $ quadrat : int 1 1 1 1 1 2 2 2 2 2 ...
## $ plant : int 1 2 3 4 5 1 2 3 4 5 ...
## $ rhi_length : num 5 5 5 4.6 5 4.6 4 5 5 3.6 ...
## $ node1 : num 0.5 0.7 0.3 0.4 0.2 1.2 0.3 0.3 0.5 0.4 ...
## $ node2 : num 0.4 0.4 0.3 0.4 0.2 0.6 0.3 0.2 0.6 0.6 ...
## $ node3 : num 0.3 0.3 0.4 0.3 0.3 0.4 0.2 0.2 0.4 0.4 ...
## $ node4 : num 0.2 0.3 0.3 0.4 0.2 0.3 0.2 0.2 0.3 0.2 ...
## $ node5 : num 0.2 0.3 0.2 0.2 0.3 0.3 0.2 0.3 0.2 0.2 ...
## $ leaf_length1 : num 22.1 25.2 16.4 21 13 32.4 23 16.5 26 26.5 ...
## $ leaf_length2 : num 18.9 17.6 9.8 18 13.6 22.1 12.3 21.1 21.1 18.1 ...
## $ leaf_length3 : num 14.5 22 14.1 8.5 9.6 25 16.1 22.7 20.2 25.7 ...
## $ leaf_length4 : num 6.4 14.9 9.4 17 NA 27.3 9.3 8.7 10.2 11.3 ...
## $ leaf_length5 : num 13.3 8 NA 16.5 NA 12.4 15.5 15.5 17.5 22.7 ...
## $ leaf_length6 : num NA 3.6 NA NA NA 9.2 NA NA NA NA ...
## $ leaf_length7 : num NA NA NA NA NA 6 NA NA NA NA ...
## $ leaf_length8 : num NA NA NA NA NA NA NA NA NA NA ...
## $ leaf_length9 : num NA NA NA NA NA NA NA NA NA NA ...
## $ leaf_length10 : num NA NA NA NA NA NA NA NA NA NA ...
## $ leaf_width1 : num 0.3 0.3 0.2 0.3 0.2 0.4 0.2 0.2 0.3 0.4 ...
## $ leaf_width2 : num 0.2 0.3 0.2 0.2 0.2 0.4 0.2 0.2 0.2 0.3 ...
## $ leaf_width3 : num 0.2 0.3 0.2 0.2 0.2 0.4 0.2 0.2 0.3 0.3 ...
## $ leaf_width4 : num 0.2 0.3 0.2 0.2 NA 0.4 0.2 0.2 0.3 0.3 ...
## $ leaf_width5 : num 0.2 0.3 NA 0.2 NA 0.2 0.3 0.3 0.2 0.4 ...
## $ leaf_width6 : num NA 0.2 NA NA NA 0.2 NA NA NA NA ...
## $ leaf_width7 : num NA NA NA NA NA 0.3 NA NA NA NA ...
## $ leaf_width8 : num NA NA NA NA NA NA NA NA NA NA ...
## $ leaf_width9 : num NA NA NA NA NA NA NA NA NA NA ...
## $ leaf_width10 : num NA NA NA NA NA NA NA NA NA NA ...
## $ pad_mass_g : num 0.543 0.529 0.536 0.491 0.592 ...
## $ pad_epiphyte_mass_g : num 0.549 0.53 0.543 0.494 0.596 ...
## $ rhi_mass_fw : num 0.227 0.488 0.346 0.397 0.455 ...
## $ rhi_foil : num 0.658 0.427 0.45 0.376 0.55 ...
## $ rhi_foil_dw_g : num 0.684 0.478 0.49 0.411 0.586 ...
## $ shoot_mass_fw : num 0.269 0.412 0.165 0.321 0.116 ...
## $ shoot_foil : num 0.905 0.728 0.758 0.611 0.708 ...
## $ shoot_foil_dw : num 0.944 0.791 0.783 0.66 0.724 ...
## $ xs_shoot_mass_fw : num 4.77 NA NA NA NA ...
## $ xs_shoot_foil : num 0.789 NA NA NA NA ...
## $ xs_shoot_foil_dw : num 1.38 NA NA NA NA ...
## $ xs_pad_mass_g : num NA NA NA NA NA NA NA NA NA NA ...
## $ xs_epiphyte_pad_mass_g: num NA NA NA NA NA NA NA NA NA NA ...
## $ iso_count : int NA NA NA NA NA NA NA NA NA NA ...
## $ iso_fw : num NA NA NA NA NA NA NA NA NA NA ...
## $ iso_foil : num NA NA NA NA NA NA NA NA NA NA ...
## $ iso_foil_dw : num NA NA NA NA NA NA NA NA NA NA ...
## $ gamm_amph_count : int 3 NA NA NA NA 21 NA NA NA NA ...
## $ gamm_amph_fw : num 0.0059 NA NA NA NA 0.036 NA NA NA NA ...
## $ gamm_amph_foil : num 0.495 NA NA NA NA ...
## $ gamm_amph_foil_dw : num 0.497 NA NA NA NA ...
## $ caprel_count : int NA NA NA NA NA NA NA NA NA NA ...
## $ caprel_fw : num NA NA NA NA NA NA NA NA NA NA ...
## $ caprel_foil : num NA NA NA NA NA NA NA NA NA NA ...
## $ caprel_foil_dw : num NA NA NA NA NA NA NA NA NA NA ...
## $ limpet_count : int NA NA NA NA NA NA NA NA NA NA ...
## $ limpet_fw : num NA NA NA NA NA NA NA NA NA NA ...
## $ limpet_foil : num NA NA NA NA NA NA NA NA NA NA ...
## $ limpet_foil_dw : num NA NA NA NA NA NA NA NA NA NA ...
## $ gastropod_count : int NA NA NA NA NA NA NA NA NA NA ...
## $ gastropod_fw : Factor w/ 80 levels "","0.0011","0.0012",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ gastropod_foil : num NA NA NA NA NA NA NA NA NA NA ...
## $ gastropod_foil_dw : num NA NA NA NA NA NA NA NA NA NA ...
## $ crab_count : int NA NA NA NA NA NA NA NA NA NA ...
## $ crab_fw : num NA NA NA NA NA NA NA NA NA NA ...
## $ crab_foil : num NA NA NA NA NA NA NA NA NA NA ...
## $ crab_foil_dw : num NA NA NA NA NA NA NA NA NA NA ...
## $ polychaete_count : int 6 NA NA NA NA 7 NA NA NA NA ...
## $ other_animal_notes : Factor w/ 33 levels "","\"crab\" is tellmesus",..: 1 1 1 1 1 12 1 1 1 1 ...
## $ notes : Factor w/ 5 levels "","caprel insignificant weight",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ shoot_dw : num 0.039 0.0629 0.0247 0.0489 0.0167 ...
## $ max_length : num 22.1 25.2 16.4 21 13.6 32.4 23 22.7 26 26.5 ...
## $ mean_length : num 22.1 25.2 16.4 21 13 32.4 23 16.5 26 26.5 ...# subset only the data that was collected before July (the main part of the growing season)
eel_sub <- eel2 %>%
filter(YYYYMMDD < 20170701)
str(eel_sub)## Classes 'rowwise_df', 'tbl_df', 'tbl' and 'data.frame': 520 obs. of 74 variables:
## $ site : Factor w/ 21 levels "2017_H_01","2017_H_02",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ collection_date : Factor w/ 18 levels "20/8/17","21/8/17",..: 18 18 18 18 18 18 18 18 18 18 ...
## $ YYYYMMDD : int 20170429 20170429 20170429 20170429 20170429 20170429 20170429 20170429 20170429 20170429 ...
## $ quadrat : int 1 1 1 1 1 2 2 2 2 2 ...
## $ plant : int 1 2 3 4 5 1 2 3 4 5 ...
## $ rhi_length : num 5 5 5 4.6 5 4.6 4 5 5 3.6 ...
## $ node1 : num 0.5 0.7 0.3 0.4 0.2 1.2 0.3 0.3 0.5 0.4 ...
## $ node2 : num 0.4 0.4 0.3 0.4 0.2 0.6 0.3 0.2 0.6 0.6 ...
## $ node3 : num 0.3 0.3 0.4 0.3 0.3 0.4 0.2 0.2 0.4 0.4 ...
## $ node4 : num 0.2 0.3 0.3 0.4 0.2 0.3 0.2 0.2 0.3 0.2 ...
## $ node5 : num 0.2 0.3 0.2 0.2 0.3 0.3 0.2 0.3 0.2 0.2 ...
## $ leaf_length1 : num 22.1 25.2 16.4 21 13 32.4 23 16.5 26 26.5 ...
## $ leaf_length2 : num 18.9 17.6 9.8 18 13.6 22.1 12.3 21.1 21.1 18.1 ...
## $ leaf_length3 : num 14.5 22 14.1 8.5 9.6 25 16.1 22.7 20.2 25.7 ...
## $ leaf_length4 : num 6.4 14.9 9.4 17 NA 27.3 9.3 8.7 10.2 11.3 ...
## $ leaf_length5 : num 13.3 8 NA 16.5 NA 12.4 15.5 15.5 17.5 22.7 ...
## $ leaf_length6 : num NA 3.6 NA NA NA 9.2 NA NA NA NA ...
## $ leaf_length7 : num NA NA NA NA NA 6 NA NA NA NA ...
## $ leaf_length8 : num NA NA NA NA NA NA NA NA NA NA ...
## $ leaf_length9 : num NA NA NA NA NA NA NA NA NA NA ...
## $ leaf_length10 : num NA NA NA NA NA NA NA NA NA NA ...
## $ leaf_width1 : num 0.3 0.3 0.2 0.3 0.2 0.4 0.2 0.2 0.3 0.4 ...
## $ leaf_width2 : num 0.2 0.3 0.2 0.2 0.2 0.4 0.2 0.2 0.2 0.3 ...
## $ leaf_width3 : num 0.2 0.3 0.2 0.2 0.2 0.4 0.2 0.2 0.3 0.3 ...
## $ leaf_width4 : num 0.2 0.3 0.2 0.2 NA 0.4 0.2 0.2 0.3 0.3 ...
## $ leaf_width5 : num 0.2 0.3 NA 0.2 NA 0.2 0.3 0.3 0.2 0.4 ...
## $ leaf_width6 : num NA 0.2 NA NA NA 0.2 NA NA NA NA ...
## $ leaf_width7 : num NA NA NA NA NA 0.3 NA NA NA NA ...
## $ leaf_width8 : num NA NA NA NA NA NA NA NA NA NA ...
## $ leaf_width9 : num NA NA NA NA NA NA NA NA NA NA ...
## $ leaf_width10 : num NA NA NA NA NA NA NA NA NA NA ...
## $ pad_mass_g : num 0.543 0.529 0.536 0.491 0.592 ...
## $ pad_epiphyte_mass_g : num 0.549 0.53 0.543 0.494 0.596 ...
## $ rhi_mass_fw : num 0.227 0.488 0.346 0.397 0.455 ...
## $ rhi_foil : num 0.658 0.427 0.45 0.376 0.55 ...
## $ rhi_foil_dw_g : num 0.684 0.478 0.49 0.411 0.586 ...
## $ shoot_mass_fw : num 0.269 0.412 0.165 0.321 0.116 ...
## $ shoot_foil : num 0.905 0.728 0.758 0.611 0.708 ...
## $ shoot_foil_dw : num 0.944 0.791 0.783 0.66 0.724 ...
## $ xs_shoot_mass_fw : num 4.77 NA NA NA NA ...
## $ xs_shoot_foil : num 0.789 NA NA NA NA ...
## $ xs_shoot_foil_dw : num 1.38 NA NA NA NA ...
## $ xs_pad_mass_g : num NA NA NA NA NA NA NA NA NA NA ...
## $ xs_epiphyte_pad_mass_g: num NA NA NA NA NA NA NA NA NA NA ...
## $ iso_count : int NA NA NA NA NA NA NA NA NA NA ...
## $ iso_fw : num NA NA NA NA NA NA NA NA NA NA ...
## $ iso_foil : num NA NA NA NA NA NA NA NA NA NA ...
## $ iso_foil_dw : num NA NA NA NA NA NA NA NA NA NA ...
## $ gamm_amph_count : int 3 NA NA NA NA 21 NA NA NA NA ...
## $ gamm_amph_fw : num 0.0059 NA NA NA NA 0.036 NA NA NA NA ...
## $ gamm_amph_foil : num 0.495 NA NA NA NA ...
## $ gamm_amph_foil_dw : num 0.497 NA NA NA NA ...
## $ caprel_count : int NA NA NA NA NA NA NA NA NA NA ...
## $ caprel_fw : num NA NA NA NA NA NA NA NA NA NA ...
## $ caprel_foil : num NA NA NA NA NA NA NA NA NA NA ...
## $ caprel_foil_dw : num NA NA NA NA NA NA NA NA NA NA ...
## $ limpet_count : int NA NA NA NA NA NA NA NA NA NA ...
## $ limpet_fw : num NA NA NA NA NA NA NA NA NA NA ...
## $ limpet_foil : num NA NA NA NA NA NA NA NA NA NA ...
## $ limpet_foil_dw : num NA NA NA NA NA NA NA NA NA NA ...
## $ gastropod_count : int NA NA NA NA NA NA NA NA NA NA ...
## $ gastropod_fw : Factor w/ 80 levels "","0.0011","0.0012",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ gastropod_foil : num NA NA NA NA NA NA NA NA NA NA ...
## $ gastropod_foil_dw : num NA NA NA NA NA NA NA NA NA NA ...
## $ crab_count : int NA NA NA NA NA NA NA NA NA NA ...
## $ crab_fw : num NA NA NA NA NA NA NA NA NA NA ...
## $ crab_foil : num NA NA NA NA NA NA NA NA NA NA ...
## $ crab_foil_dw : num NA NA NA NA NA NA NA NA NA NA ...
## $ polychaete_count : int 6 NA NA NA NA 7 NA NA NA NA ...
## $ other_animal_notes : Factor w/ 33 levels "","\"crab\" is tellmesus",..: 1 1 1 1 1 12 1 1 1 1 ...
## $ notes : Factor w/ 5 levels "","caprel insignificant weight",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ shoot_dw : num 0.039 0.0629 0.0247 0.0489 0.0167 ...
## $ max_length : num 22.1 25.2 16.4 21 13.6 32.4 23 22.7 26 26.5 ...
## $ mean_length : num 22.1 25.2 16.4 21 13 32.4 23 16.5 26 26.5 ...levels(as.factor(eel_sub$YYYYMMDD))## [1] "20170429" "20170524" "20170525" "20170528" "20170622" "20170623"
## [7] "20170624" "20170625" "20170627" "20170628"summary(eel_sub)## site collection_date YYYYMMDD quadrat
## 2017_H_01: 40 29/4/17:120 Min. :20170429 Min. :1.00
## 2017_H_02: 40 25/5/17: 80 1st Qu.:20170524 1st Qu.:2.75
## 2017_H_03: 40 22/6/17: 40 Median :20170528 Median :4.50
## 2017_H_04: 40 23/6/17: 40 Mean :20170549 Mean :4.50
## 2017_L_01: 40 24/5/17: 40 3rd Qu.:20170624 3rd Qu.:6.25
## 2017_L_02: 40 24/6/17: 40 Max. :20170628 Max. :8.00
## (Other) :280 (Other):160
## plant rhi_length node1 node2
## Min. :1 Min. :0.800 Min. :0.100 Min. :0.100
## 1st Qu.:2 1st Qu.:4.300 1st Qu.:0.600 1st Qu.:0.600
## Median :3 Median :5.000 Median :0.900 Median :1.100
## Mean :3 Mean :4.439 Mean :1.018 Mean :1.196
## 3rd Qu.:4 3rd Qu.:5.000 3rd Qu.:1.300 3rd Qu.:1.600
## Max. :5 Max. :5.000 Max. :3.000 Max. :3.600
## NA's :7 NA's :7 NA's :19
## node3 node4 node5 leaf_length1
## Min. :0.1000 Min. :0.1000 Min. :0.1000 Min. : 2.30
## 1st Qu.:0.4000 1st Qu.:0.3000 1st Qu.:0.2000 1st Qu.: 19.00
## Median :0.7000 Median :0.5000 Median :0.3000 Median : 28.40
## Mean :0.8411 Mean :0.5618 Mean :0.3634 Mean : 33.15
## 3rd Qu.:1.2000 3rd Qu.:0.7000 3rd Qu.:0.5000 3rd Qu.: 42.40
## Max. :2.7000 Max. :8.0000 Max. :1.5000 Max. :105.20
## NA's :116 NA's :206 NA's :293 NA's :7
## leaf_length2 leaf_length3 leaf_length4 leaf_length5
## Min. : 1.70 Min. : 3.10 Min. : 2.10 Min. : 1.90
## 1st Qu.: 18.30 1st Qu.: 14.45 1st Qu.:15.55 1st Qu.: 15.65
## Median : 30.30 Median : 26.00 Median :28.00 Median : 26.40
## Mean : 33.83 Mean : 31.11 Mean :31.02 Mean : 29.54
## 3rd Qu.: 46.20 3rd Qu.: 43.10 3rd Qu.:41.90 3rd Qu.: 39.25
## Max. :110.50 Max. :109.90 Max. :96.00 Max. :111.70
## NA's :7 NA's :8 NA's :22 NA's :117
## leaf_length6 leaf_length7 leaf_length8 leaf_length9
## Min. : 2.10 Min. : 1.30 Min. : 2.600 Min. : 3.00
## 1st Qu.:12.53 1st Qu.:14.45 1st Qu.: 7.475 1st Qu.: 8.25
## Median :27.10 Median :25.00 Median :21.000 Median :14.05
## Mean :30.06 Mean :28.79 Mean :27.190 Mean :19.22
## 3rd Qu.:40.35 3rd Qu.:41.15 3rd Qu.:36.900 3rd Qu.:28.02
## Max. :94.20 Max. :82.60 Max. :97.200 Max. :45.20
## NA's :310 NA's :433 NA's :490 NA's :514
## leaf_length10 leaf_width1 leaf_width2 leaf_width3
## Min. :16.00 Min. :0.1000 Min. :0.100 Min. :0.1000
## 1st Qu.:18.18 1st Qu.:0.3000 1st Qu.:0.300 1st Qu.:0.3000
## Median :20.35 Median :0.4000 Median :0.400 Median :0.4000
## Mean :20.35 Mean :0.3739 Mean :0.383 Mean :0.3918
## 3rd Qu.:22.52 3rd Qu.:0.5000 3rd Qu.:0.500 3rd Qu.:0.5000
## Max. :24.70 Max. :0.8000 Max. :0.800 Max. :0.8000
## NA's :518 NA's :7 NA's :7 NA's :8
## leaf_width4 leaf_width5 leaf_width6 leaf_width7
## Min. :0.1000 Min. :0.100 Min. :0.1000 Min. :0.2000
## 1st Qu.:0.3000 1st Qu.:0.300 1st Qu.:0.3000 1st Qu.:0.3000
## Median :0.4000 Median :0.400 Median :0.4000 Median :0.4000
## Mean :0.3946 Mean :0.394 Mean :0.3857 Mean :0.3851
## 3rd Qu.:0.5000 3rd Qu.:0.500 3rd Qu.:0.5000 3rd Qu.:0.4000
## Max. :0.8000 Max. :0.800 Max. :0.7000 Max. :0.7000
## NA's :22 NA's :117 NA's :310 NA's :433
## leaf_width8 leaf_width9 leaf_width10 pad_mass_g
## Min. :0.2000 Min. :0.2000 Min. :0.300 Min. :0.4728
## 1st Qu.:0.3000 1st Qu.:0.3000 1st Qu.:0.325 1st Qu.:0.6298
## Median :0.4000 Median :0.3000 Median :0.350 Median :0.6655
## Mean :0.3867 Mean :0.3167 Mean :0.350 Mean :0.6603
## 3rd Qu.:0.4000 3rd Qu.:0.3750 3rd Qu.:0.375 3rd Qu.:0.7017
## Max. :0.6000 Max. :0.4000 Max. :0.400 Max. :0.8519
## NA's :490 NA's :514 NA's :518 NA's :17
## pad_epiphyte_mass_g rhi_mass_fw rhi_foil rhi_foil_dw_g
## Min. :0.4806 Min. :0.0353 Min. :0.2876 Min. :0.3297
## 1st Qu.:0.6351 1st Qu.:0.3652 1st Qu.:0.7200 1st Qu.:0.7841
## Median :0.6735 Median :0.5143 Median :0.8724 Median :0.9502
## Mean :0.6667 Mean :0.5399 Mean :0.9020 Mean :0.9759
## 3rd Qu.:0.7066 3rd Qu.:0.6996 3rd Qu.:1.1072 3rd Qu.:1.1889
## Max. :0.9698 Max. :1.4428 Max. :1.4849 Max. :1.6390
## NA's :17 NA's :7 NA's :7 NA's :7
## shoot_mass_fw shoot_foil shoot_foil_dw xs_shoot_mass_fw
## Min. :0.0293 Min. :0.4336 Min. :0.4864 Min. : 0.0243
## 1st Qu.:0.5250 1st Qu.:1.0716 1st Qu.:1.2224 1st Qu.: 2.9386
## Median :1.0255 Median :1.3160 Median :1.5332 Median : 6.2291
## Mean :1.3357 Mean :1.3830 Mean :1.5803 Mean :10.4281
## 3rd Qu.:1.7271 3rd Qu.:1.7837 3rd Qu.:2.0987 3rd Qu.:13.7739
## Max. :6.6599 Max. :2.8118 Max. :2.9439 Max. :66.8455
## NA's :7 NA's :7 NA's :7 NA's :416
## xs_shoot_foil xs_shoot_foil_dw xs_pad_mass_g
## Min. :0.5372 Min. : 0.6509 Min. :0.7107
## 1st Qu.:1.1943 1st Qu.: 1.8682 1st Qu.:0.7107
## Median :1.5204 Median : 2.4118 Median :0.7107
## Mean :1.8368 Mean : 3.1024 Mean :0.7107
## 3rd Qu.:2.0028 3rd Qu.: 3.2686 3rd Qu.:0.7107
## Max. :5.3852 Max. :12.5068 Max. :0.7107
## NA's :416 NA's :416 NA's :519
## xs_epiphyte_pad_mass_g iso_count iso_fw iso_foil
## Min. :0.7444 Min. :1.000 Min. :0.0002 Min. :0.3042
## 1st Qu.:0.7444 1st Qu.:1.000 1st Qu.:0.0050 1st Qu.:0.4584
## Median :0.7444 Median :1.000 Median :0.1364 Median :0.5225
## Mean :0.7444 Mean :1.487 Mean :0.1862 Mean :0.5613
## 3rd Qu.:0.7444 3rd Qu.:2.000 3rd Qu.:0.2212 3rd Qu.:0.6674
## Max. :0.7444 Max. :5.000 Max. :1.2169 Max. :0.9067
## NA's :519 NA's :481 NA's :481 NA's :481
## iso_foil_dw gamm_amph_count gamm_amph_fw gamm_amph_foil
## Min. :0.3756 Min. : 1.000 Min. :0.0004 Min. :0.2773
## 1st Qu.:0.4948 1st Qu.: 2.000 1st Qu.:0.0046 1st Qu.:0.4560
## Median :0.5599 Median : 3.000 Median :0.0075 Median :0.5098
## Mean :0.6097 Mean : 5.678 Mean :0.0147 Mean :0.5325
## 3rd Qu.:0.7326 3rd Qu.: 8.000 3rd Qu.:0.0140 3rd Qu.:0.5708
## Max. :0.9509 Max. :33.000 Max. :0.1460 Max. :1.0428
## NA's :481 NA's :461 NA's :462 NA's :462
## gamm_amph_foil_dw caprel_count caprel_fw caprel_foil
## Min. :0.2779 Min. : 1.000 Min. :0.0003 Min. :0.0001
## 1st Qu.:0.4568 1st Qu.: 1.000 1st Qu.:0.0026 1st Qu.:0.4412
## Median :0.5100 Median : 2.000 Median :0.0074 Median :0.5155
## Mean :0.5336 Mean : 4.979 Mean :0.0424 Mean :0.5176
## 3rd Qu.:0.5727 3rd Qu.: 5.000 3rd Qu.:0.0234 3rd Qu.:0.6414
## Max. :1.0438 Max. :28.000 Max. :0.6490 Max. :1.0819
## NA's :462 NA's :472 NA's :476 NA's :473
## caprel_foil_dw limpet_count limpet_fw limpet_foil
## Min. :0.0002 Min. : 1.000 Min. :0.0037 Min. :0.2127
## 1st Qu.:0.4434 1st Qu.: 2.000 1st Qu.:0.0216 1st Qu.:0.4171
## Median :0.5183 Median : 3.000 Median :0.0464 Median :0.5199
## Mean :0.5195 Mean : 4.181 Mean :0.1183 Mean :0.5230
## 3rd Qu.:0.6454 3rd Qu.: 5.250 3rd Qu.:0.1445 3rd Qu.:0.6012
## Max. :1.0860 Max. :15.000 Max. :0.8388 Max. :1.0519
## NA's :473 NA's :448 NA's :448 NA's :448
## limpet_foil_dw gastropod_count gastropod_fw
## Min. :0.2152 Min. : 1.000 :478
## 1st Qu.:0.4538 1st Qu.: 1.250 0.0037 : 2
## Median :0.5500 Median : 2.000 0.0056 : 2
## Mean :0.5587 Mean : 3.405 limpet = Lottia parallela: 2
## 3rd Qu.:0.6429 3rd Qu.: 4.000 0.0011 : 1
## Max. :1.0611 Max. :16.000 0.0012 : 1
## NA's :448 NA's :478 (Other) : 34
## gastropod_foil gastropod_foil_dw crab_count crab_fw
## Min. :0.0001 Min. :0.0002 Min. : NA Min. : NA
## 1st Qu.:0.3992 1st Qu.:0.4186 1st Qu.: NA 1st Qu.: NA
## Median :0.5174 Median :0.5216 Median : NA Median : NA
## Mean :0.5256 Mean :0.5394 Mean :NaN Mean :NaN
## 3rd Qu.:0.6185 3rd Qu.:0.6308 3rd Qu.: NA 3rd Qu.: NA
## Max. :1.0419 Max. :1.0514 Max. : NA Max. : NA
## NA's :478 NA's :478 NA's :520 NA's :520
## crab_foil crab_foil_dw polychaete_count other_animal_notes
## Min. : NA Min. : NA Min. : 1.000 :490
## 1st Qu.: NA 1st Qu.: NA 1st Qu.: 3.000 1 bivalve : 7
## Median : NA Median : NA Median : 4.000 2 bivalves: 4
## Mean :NaN Mean :NaN Mean : 5.267 1 clam : 3
## 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: 6.000 1 barnacle: 2
## Max. : NA Max. : NA Max. :35.000 3 bivalves: 2
## NA's :520 NA's :520 NA's :419 (Other) : 12
## notes shoot_dw
## :517 Min. :0.0042
## caprel insignificant weight : 0 1st Qu.:0.0938
## caprel so small it was not measurable : 1 Median :0.1576
## FW missing from gastropod : 1 Mean :0.1973
## gastropod lost, DW unknow : 1 3rd Qu.:0.2575
## Max. :0.8045
## NA's :7
## max_length mean_length
## Min. : -Inf Min. : 2.30
## 1st Qu.: 29.27 1st Qu.: 19.00
## Median : 43.40 Median : 28.40
## Mean : -Inf Mean : 33.15
## 3rd Qu.: 57.23 3rd Qu.: 42.40
## Max. :111.70 Max. :105.20
## NA's :7eel_sub2 <- na.omit(data.frame(eel_sub$shoot_dw, eel_sub$max_length, eel_sub$mean_length, eel_sub$shoot_mass_fw))
names(eel_sub2) <- c("shoot_dw", "max_length", "mean_length", "shoot_mass_fw")Look at data
plot(eel_sub2$max_length, eel_sub2$shoot_dw)
# Looks like the variance in shoot dw might be increasing with the the length (mm) of the longest blade.
plot(eel_sub2$mean_length, eel_sub2$shoot_dw)
plot(eel_sub2$max_length, eel_sub2$shoot_mass_fw)
# not as tight as a correlation
cor(eel_sub2$max_length, eel_sub2$shoot_dw, use = "complete.obs")## [1] 0.8782586cor(eel_sub2$mean_length, eel_sub2$shoot_dw, use = "complete.obs")## [1] 0.66646cor(eel_sub2$mean_length, eel_sub2$shoot_mass_fw, use = "complete.obs")## [1] 0.6754984# stronger positive linear relationship with max length than average length (makes sense)
par(mfrow=c(1,2))
boxplot(eel_sub2$max_length)
boxplot(eel_sub2$shoot_dw)
# Looks like both varibles may have some outliers, in particular the shoot dry weight.
par(mfrow=c(1,2))
plot(density(eel_sub2$max_length), ylab = "Frequency")
plot(density(eel_sub2$shoot_dw, na.rm = T), ylab = "Frequency")
hist(eel_sub2$max_length)
# max length looks pretty normal with some slight right? skew
hist(eel_sub2$shoot_dw)
# heavily right skewed does log make it look better?
hist(log(eel_sub2$shoot_dw))
# skews it the other way... 
Linear models
data only before july
dat <- na.omit(data.frame(eel_sub2$shoot_dw, eel_sub2$max_length))
names(dat) <- c("dw", "max_length")
plot(dat$dw, dat$max_length)
# Lets fit an untransformed linear model
fit.lm <- lm(dw ~ max_length, data = dat)
plot(fit.lm)
# looks like there might be some funky stuff happening with the residuals -- maybe an increase in variance along the fitted values. And the QQ plot looks real gross with some concavity (i.e. skewness) and some possible outliers. The cooks plot makes it look like there case numbers (171, 230, 498) could be influencial outliers. Some of this is consisten with the histogram of the data (see above)
summary(fit.lm)##
## Call:
## lm(formula = dw ~ max_length, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.20351 -0.04010 -0.00246 0.02434 0.52698
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0917116 0.0076619 -11.97 <2e-16 ***
## max_length 0.0062482 0.0001505 41.52 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07254 on 511 degrees of freedom
## Multiple R-squared: 0.7713, Adjusted R-squared: 0.7709
## F-statistic: 1724 on 1 and 511 DF, p-value: < 2.2e-16AIC(fit.lm); BIC(fit.lm)## [1] -1232.068## [1] -1219.347# looks like max length is significantly different than 0
# Has an adjusted r2 of 69%... pretty good for ecology. Model is significant, coefficients for the model are not equal to zero (p value =~0)
library(e1071)
skewness(dat$dw)## [1] 1.654238skewness(dat$max_length)## [1] 0.8226692# the response variable is skewed.... highly skewed
e <- residuals(fit.lm)
shapiro.test(e)##
## Shapiro-Wilk normality test
##
## data: e
## W = 0.87574, p-value < 2.2e-16n <- length(dat$dw)
ggplot(dat, aes(x = max_length, y = dw)) + geom_point() + theme_classic() +
geom_smooth(method = "lm", se = TRUE)
# Does a boxcox indicate a transformation is necessary?
library(MASS)
boxcox(fit.lm)
boxcox(fit.lm, lambda=seq(from=0, to=0.6, by=.01)) 
# Umm of like square root?
# Could also try a log
hist(dat$dw^0.5)
y.2 <- dat$y^(0.5)
# looks way better with a fourth root transformation, normal distribution
fit2 <- lm(dw^(0.5) ~ max_length, data = dat)
plot(fit2)
# looks a lot better, homoscedascity is good, qq plot tails are a little weird, still some outliers
summary(fit2)##
## Call:
## lm(formula = dw^(0.5) ~ max_length, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.19936 -0.04459 -0.00439 0.03367 0.41670
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1044604 0.0071964 14.52 <2e-16 ***
## max_length 0.0067145 0.0001414 47.50 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.06813 on 511 degrees of freedom
## Multiple R-squared: 0.8154, Adjusted R-squared: 0.815
## F-statistic: 2256 on 1 and 511 DF, p-value: < 2.2e-16# r squared is better, model is still significant
plot(dat$max_length, dat$dw^(0.5))
# Plot the square root transformed data
lm_eqn <- function(dat){
m <- fit2;
eq <- substitute(italic(sqrt(y)) == a + b %.% italic(x)*","~~italic(r)^2~"="~r2,
list(a = format(unname(coef(m)[1]), digits = 2),
b = format(unname(coef(m)[2]), digits = 2),
r2 = format(summary(m)$r.squared, digits = 3)))
as.character(as.expression(eq));
}
ggplot(dat, aes(x = max_length, y = dw^(0.5))) + geom_point() + geom_smooth(method = "lm", se = TRUE, level = 0.95) + geom_text(x = 25, y = 0.8, label = lm_eqn(dat), parse = TRUE, check_overlap = TRUE) + theme_classic()
# Compare to a logtransformed model (easier to understand)
fit3 <- lm(log(dw) ~ max_length, data = dat)
plot(fit3)
summary(fit3)##
## Call:
## lm(formula = log(dw) ~ max_length, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.26096 -0.21548 0.03841 0.26030 1.43709
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.4469963 0.0443016 -77.81 <2e-16 ***
## max_length 0.0331391 0.0008702 38.08 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4194 on 511 degrees of freedom
## Multiple R-squared: 0.7395, Adjusted R-squared: 0.739
## F-statistic: 1450 on 1 and 511 DF, p-value: < 2.2e-16hist(log(dat$dw))
plot(dat$max_length, log(dat$dw))
# log-log transformation
lm2 <- lm(log(shoot_dw) ~ log(max_length), data = eel_sub2)
summary(lm2)##
## Call:
## lm(formula = log(shoot_dw) ~ log(max_length), data = eel_sub2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.0858 -0.2056 0.0128 0.1736 1.2739
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.78423 0.11222 -69.37 <2e-16 ***
## log(max_length) 1.57549 0.02987 52.74 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3237 on 511 degrees of freedom
## Multiple R-squared: 0.8448, Adjusted R-squared: 0.8445
## F-statistic: 2781 on 1 and 511 DF, p-value: < 2.2e-16plot(lm2)
# plot the log-log transformed data with linear model
lm_eqn <- function(dat){
m <- lm2;
eq <- substitute(italic(log(y)) == a + b %.% italic(log(x))*","~~italic(r)^2~"="~r2,
list(a = format(unname(coef(m)[1]), digits = 2),
b = format(unname(coef(m)[2]), digits = 2),
r2 = format(summary(m)$r.squared, digits = 3)))
as.character(as.expression(eq));
}
ggplot(dat, aes(x = log(max_length), y = log(dw))) + geom_point() + geom_smooth(method = "lm", se = TRUE, level = 0.95) + geom_text(x = 2.5, y = -0.5, label = lm_eqn(dat), parse = TRUE, check_overlap = TRUE) +
theme_classic()
visreg(lm2, gg = T)
Use selected linear model to predict 2019 biomass data
Import 2019 data
The data for 2019: is set up in a 3 sheet excel spreadsheets. quadrat_ID in hab_qrt is unique for every quadrat that was done in summer 2019. Use the quadrat_ID to connect to the other sheets (hab_lng and hab_wgt). Hab_lng has all the individual lengths for all measured plants (in mm) (for eelgrass its 15 blades from each quadrat/site. For kelp its up to 3 of each species collected from the site). Hab_wgt has the biomass weights for individual species biomass by bag
For this purpose we are interested in only the length data but need to use the quadrat sheet to make sure we only are looking at the eelgrass sites newest version of data is : RAW_10-22-19
hab_qrt <- read_excel("../ALL_DATA/Lia_fish/Habitat_2019_RAW_10-23-19.xlsx", sheet = 1)
hab_lng <- read_excel("../ALL_DATA/Lia_fish/Habitat_2019_RAW_10-23-19.xlsx", sheet = 2)
hab_wgt <- read_excel("../ALL_DATA/Lia_fish/Habitat_2019_RAW_10-23-19.xlsx", sheet = 3)Data cleaning
We want only the eelgrass sites and need to convert the length measurements (in mm) to cm
glimpse(hab_qrt)## Observations: 125
## Variables: 10
## $ quadrat_ID <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, …
## $ site <chr> "Shakan - kelp", "Shakan - kelp", "Shakan - kel…
## $ date <dttm> 2019-06-08, 2019-06-08, 2019-06-08, 2019-06-08…
## $ YYYYMMDD <dbl> 20190608, 20190608, 20190608, 20190608, 2019060…
## $ habitat <chr> "kelp", "kelp", "kelp", "kelp", "kelp", "seagra…
## $ quadrat <dbl> 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1,…
## $ total_biomass <chr> "620.9", "230.4", "869.4", "716.0", "748.7", "N…
## $ density <chr> "NA", "NA", "NA", "NA", "NA", "29.0", "18.0", "…
## $ flowering_shoots <chr> "NA", "NA", "NA", "NA", "NA", "0.0", "0.0", "0.…
## $ notes <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…# Want to only extract the quadrat numbers that were used to sample eelgrass sites
eel_qrts <- na.omit(ifelse(hab_qrt$habitat == "seagrass", paste(hab_qrt$quadrat_ID), NA))
# subset the length data by quadrat at an eelgrass site
lng_sub <- subset(hab_lng, quadrat_ID %in% eel_qrts)
levels(as.factor(lng_sub$species)) # only Z. marina, awesome! ## [1] "Zostera marina"lng_sub <- lng_sub %>%
mutate(length_cm = length/100)Predict based on linear models
summary(lm2)##
## Call:
## lm(formula = log(shoot_dw) ~ log(max_length), data = eel_sub2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.0858 -0.2056 0.0128 0.1736 1.2739
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.78423 0.11222 -69.37 <2e-16 ***
## log(max_length) 1.57549 0.02987 52.74 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3237 on 511 degrees of freedom
## Multiple R-squared: 0.8448, Adjusted R-squared: 0.8445
## F-statistic: 2781 on 1 and 511 DF, p-value: < 2.2e-16# formula :
# log (y) = beta0 + beta1 * log (x)
# log (dw) = -7.78 + 1.57 * log (max_length)
# create a new dataframe with the leaf lengths from 2019
newx <- data.frame(max_length = as.numeric(lng_sub$length_cm))
# use the log-log linear model (equation above) to calculate the dry mass of the shoot with 95% CI
# make sure to exp() the values because its a log-log relationship
pr.lm <- exp(predict(lm2, newdata = newx, interval = "confidence", level = 0.95))
# graph these new predicted values
# create data.frame first
newdata <- cbind(lng_sub, pr.lm)
ggplot(newdata, aes(x = length_cm, y = fit)) + geom_point() + theme_classic() + geom_smooth(method = "lm")
Estimate total biomass
str(newdata)## 'data.frame': 1503 obs. of 8 variables:
## $ plant_ID : num 27 28 29 30 31 32 33 34 35 36 ...
## $ quadrat_ID: num 6 6 6 6 6 6 6 6 6 6 ...
## $ species : chr "Zostera marina" "Zostera marina" "Zostera marina" "Zostera marina" ...
## $ length : num 579 835 375 334 691 323 581 418 852 544 ...
## $ length_cm : num 5.79 8.35 3.75 3.34 6.91 3.23 5.81 4.18 8.52 5.44 ...
## $ fit : num 0.00662 0.01179 0.00334 0.00278 0.00875 ...
## $ lwr : num 0.00588 0.01069 0.00289 0.00239 0.00785 ...
## $ upr : num 0.00746 0.01301 0.00386 0.00324 0.00976 ...str(hab_qrt)## Classes 'tbl_df', 'tbl' and 'data.frame': 125 obs. of 10 variables:
## $ quadrat_ID : num 1 2 3 4 5 6 7 8 9 10 ...
## $ site : chr "Shakan - kelp" "Shakan - kelp" "Shakan - kelp" "Shakan - kelp" ...
## $ date : POSIXct, format: "2019-06-08" "2019-06-08" ...
## $ YYYYMMDD : num 20190608 20190608 20190608 20190608 20190608 ...
## $ habitat : chr "kelp" "kelp" "kelp" "kelp" ...
## $ quadrat : num 1 2 3 4 5 1 2 3 4 5 ...
## $ total_biomass : chr "620.9" "230.4" "869.4" "716.0" ...
## $ density : chr "NA" "NA" "NA" "NA" ...
## $ flowering_shoots: chr "NA" "NA" "NA" "NA" ...
## $ notes : chr NA NA NA NA ...hab_qrt$density <- as.numeric(hab_qrt$density)## Warning: NAs introduced by coercionhab_qrt$flowering_shoots <- as.numeric(hab_qrt$flowering_shoots)## Warning: NAs introduced by coercion# Want to only extract the quadrat numbers that were used to sample eelgrass sites
dsty <- hab_qrt %>%
filter(habitat == 'seagrass') %>%
mutate(density_m2 = (density)*4) %>%
mutate(flowering_m2 = (flowering_shoots)*4)
dsty <- dsty[,c(1,8,9,11,12)]
# calculate average biomass for 15 shoots for each quadrat
require(dplyr)
df <- newdata %>%
group_by(quadrat_ID) %>%
mutate(avg_biomass = mean(fit))
df1 <- df %>%
dplyr::select(quadrat_ID, avg_biomass) %>%
distinct()
# need to add back in site information
df2 <- left_join(df1, dsty, by = "quadrat_ID")
df3 <- left_join(df2, hab_qrt[,1:2], by = "quadrat_ID")
write.csv(df3, "../ALL_DATA/seagrass_biomass_conversions.csv")